text-graph-generalization

Code for our paper Compositional Generalization with Grounded Language Models
git clone https://esimon.eu/repos/text-graph-generalization.git
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commit e766fc2acc91f39c0227f806eb67c03815653914
parent 11029922807904507a66b90434f08ae431b2cf10
Author: Sondre Wold <sondrewold1@gmail.com>
Date:   Thu, 30 May 2024 15:31:43 +0200

Update README.md

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MREADME.md | 6++++--
1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md @@ -52,8 +52,7 @@ Run `dataset.py` with `--task` being either `sys`, `subst` or `prod` to create the datasets used for the three experiments from the paper. ### Running the dataset splits -Run `train.py` with your desiered parameters. Consult the `argparse` for an -explantion of the CLI. The file runs the experiments over a set of seeds +`train.py` runs all the experiments on the provided dataset splits. An axplantion of the CLI can be found in the `argpase` method in this file. The file runs the experiments over a set of seeds provided by a white space seperated list to `--seed`. The different models are selected by setting the following model-specific @@ -64,6 +63,9 @@ flags: - Unidirectional: `--unidirectional` - Bidirectional: `--unidirectional --bidirectional` +The hyperparameters we used for the experiments in the paper can be found +in Appendix B. + ### Results Results are logged to Wandb and written to a json file. You can use ```write_results.py``` to aggreate the results across seeds and